Xanthan gum: Secondary raw materials for biosynthesis, isolation and application

Food systems, Год журнала: 2025, Номер 7(4), С. 515 - 522

Опубликована: Фев. 1, 2025

The inevitable consequence of population growth is the development agriculture and food production, which in turn has an impact on volumes secondary raw materials production. processing these can present significant challenges. One most effective solutions to this problem use microbiological synthesis create products with high added value. A notable example xanthan gum, a biopolymer that been utilized multitude industries, including food, oil, pharmaceutical, medicine. value gum contingent upon its distinctive physicochemical properties, particularly capacity enhance viscosity solutions. process obtaining conducted through fermentation liquid high-carbon media. primary producer bacterium Xanthomonas campestris , phytopathogen cruciferous plants, converts carbohydrates into commercial This literature review examines several topics related by X. particular attention paid success target product using production waste agricultural materials.

Язык: Английский

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Machine learning-based biological process optimization for low molecular weight welan gum production

International Journal of Biological Macromolecules, Год журнала: 2025, Номер unknown, С. 142177 - 142177

Опубликована: Март 1, 2025

Язык: Английский

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Integrating waste management with biopolymer innovation: A new frontier in xanthan gum production

Process Biochemistry, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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Application of Machine Learning for Bit-formation Matching in Drilling Operations

Petroleum Science and Engineering, Год журнала: 2025, Номер 9(1), С. 38 - 47

Опубликована: Май 29, 2025

Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations emissions reduction to provide energy solutions. Currently, drill selection predominantly depends on historical data experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in selection, other diverse impactful algorithms such as XGBOOST Random Forest (RF), are often overlooked. This paper involves systematic application comparative analysis XGBOOST, RF, ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including properties, fluid characteristics, design, operational parameters. In this study, we achieved promising results with highest classification accuracy recorded at 0.97 model, while RF ANN yielded accuracies 0.91 0.93 respectively. Additionally, obtained impressive R squared values 0.991, 0.975, 0.953 predicting Rate Penetration models These algorithms, coupled techniques, aim establish a robust framework nuanced accurate bit-formation matching. hold significant potential minimizing costs optimizing resource allocation utilization during planning execution projects oil gas industry.

Язык: Английский

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Machine learning-optimized bioprocess for macroidin production by Lysinibacillus macroides and its biomedical applications

Bioprocess and Biosystems Engineering, Год журнала: 2025, Номер unknown

Опубликована: Июнь 4, 2025

Язык: Английский

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Surfactant-facilitated metabolic induction enhances lipase production from an optimally formulated waste-derived substrate mix using Aspergillus niger: A case of machine learning modeling and metaheuristic optimization

Bioresource Technology Reports, Год журнала: 2024, Номер unknown, С. 101993 - 101993

Опубликована: Ноя. 1, 2024

Язык: Английский

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A machine learning-supported framework for predicting Nigeria’s optimal energy storage and emission reduction potentials

Renewable energy focus, Год журнала: 2024, Номер unknown, С. 100677 - 100677

Опубликована: Дек. 1, 2024

Язык: Английский

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Characterization and evaluation of the adsorption of uremic toxins through the pyrolysis of pineapple leaves and peels and by forming a bio-complex with sodium alginate

International Journal of Biological Macromolecules, Год журнала: 2024, Номер unknown, С. 138843 - 138843

Опубликована: Дек. 1, 2024

Язык: Английский

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